Abstract: In one embodiment, a method includes, by an electronic device, accessing activity data containing one or more non-image-based sensor signals from a first wearable device, where the activity data corresponds to an activity a user performs during a first timeframe, accessing from a first camera device, one or more cameras of the first camera device, where the video data corresponds to the first activity of the first user during the first timeframe, segmenting the activity data based on one or more features of the one or more non-image-based sensor signals to identify one or more segments of activity data corresponding to a second timeframe, classifying the one or more segments of the video data based on the one or more identified events associated with the first activity during the second timeframe, classifying the segments of the video data based on the one or more events during the second timeframe.

Abstract: In one embodiment, a method includes accessing an initial image and applying each image filter from a set of one or more N image filters to the initial image to create N filtered images. The method further includes generating a similarity metric for each pair of images in a set of images comprising the initial image and the N filtered images and determining, based on the similarity metrics, a placement of each image in the set of images within an image-filter space. The method further includes generating, based on the image-filter space, a selectable image-filter interface; and providing the selectable image-filter interface for display, wherein each selectable region of the selectable image-filter interface is associated with a particular filtering of the initial image.

Abstract: In one embodiment, a method includes maintaining a video communication between two or more client devices with each client device comprising cameras and being associated with a respective video stream in the video communication, determining scene data within a field of view in a real-world environment captured by one or more of the cameras of a first client device of the two or more client devices, determining a privacy filter to apply to a first video stream associated with the first client device based on the scene data, and providing instructions to apply the privacy filter to the first video stream in the video communication.

Abstract: In one embodiment, a method includes maintaining a video communication between client devices with each being associated with a respective video stream, which is associated with a respective match scale measured based on a height of frames of the video stream and a depth of subjects within the frames, determining a respective scaling factor and cropping for each video stream, wherein the respective scaling factor is calculated based on the match scale associated with the corresponding video stream and a target match scale determined from the match scales associated with all video streams, and wherein the respective cropping is determined based on a uniformity of positions of the subjects within the frames across all video streams, generating a merged video stream from each video stream based on the respective scaling factor and cropping, and sending instructions for presenting the merged video stream to one or more of the client devices.

Abstract: In one embodiment, a method includes, by an electronic device, receiving a digital input indicating a type of movement. Streaming sensor data corresponding to movements of a first user can be accessed from one or more sensors of one or more wearable devices on the first user. Displacement data corresponding to the movements of the first user can be generated based on the streaming sensor data and the indicated type of movement. One or more repetitions represented in one or more corresponding segments of the displacement data can be identified based on the displacement data. The displacement data corresponding to each identified repetition may substantially match a predetermined movement signature associated with the type of movement. One or more performance metrics can be calculated for one or more of the identified repetitions. Instructions can be provided for presenting a user interface including information referencing the performance metrics.

Abstract: In one embodiment, a method includes, by an electronic device, accessing activity data containing one or more non-image-based sensor signals from a first wearable device, where the activity data corresponds to an activity a user performs during a first timeframe, accessing from a first camera device, one or more cameras of the first camera device, where the video data corresponds to the first activity of the first user during the first timeframe, segmenting the activity data based on one or more features of the one or more non-image-based sensor signals to identify one or more segments of activity data corresponding to a second timeframe, classifying the one or more segments of the video data based on the one or more identified events associated with the first activity during the second timeframe, classifying the segments of the video data based on the one or more events during the second timeframe.

Abstract: Some embodiments provide a program that receives a request to apply a hierarchical rule definition to a set of data objects. Each data object in the set of data objects includes a set of attributes. The program further retrieves the hierarchical rule definition from a set of hierarchical rule definitions. Each hierarchical rule definition in the set of hierarchical rule definitions includes a hierarchy of rules. Each rule in the hierarchy of rules includes a set of criteria and a set of actions that are performed when the set of criteria are satisfied. The program also applies the hierarchical rule definition to each data object in a subset of the set of data objects by traversing the hierarchy of rules of the hierarchical rule definition and modifying the data object based on sets of criteria and sets of actions specified in the hierarchy of rules.